This invention relates to adaptive sensors. More particularly, this invention relates to sensors capable of detecting a wide-range of conditions or events and deployable with minimal development because the sensor output is generated initially with the assistance of crowd workers until machine-learning aspects of the sensor system are able to provide accurate output.
For decades, “smart” environments have promised to improve our lives by inferring context, activity, and events in diverse environments, ranging from public spaces, offices, and labs, to homes and healthcare facilities. To achieve this vision, smart environments require sensors, and many of them. These systems are expensive because they require specific sensors to be placed throughout the environment. In addition, these systems are special purpose and often invasive (e.g., running power to several sensors).
An even more challenging problem is that sensor output rarely matches the types of questions humans wish to ask and expected of an intelligent environment. For example, a door opened/closed sensor may not answer the user's true question: “Are my children home from school?” Similarly, a restaurateur may want to know: “How many patrons need their beverages refilled?” and graduate students want to know “Is there free food in the kitchenette?”
Unfortunately, these sophisticated, multidimensional, and often contextual questions are not easily answered by the simple sensors deployed today. Although advances in sensing, computer vision (CV), and machine learning (ML) have improved sensing abilities, systems that generalize across these broad and dynamic contexts do not yet exist, while less adaptive systems are expensive and complicated. For example, some smart environments monitor instrumented space (scanners, RFID tags, etc.), making the system specific to a particular question to be answered and expensive due to the number of sensors and other equipment. It would therefore be advantageous to develop a sensor system that answers a broad range of user-defined questions based on real-time sensor feeds with reduced complexity and costs.